2.1 Subset
2.2 Foundational Dateframes & Considerations
mention that we will look at two basic subsets: One general one with all obrservations of the initial subset, and one based on the context of the keywords. Justify why.
mention events etc.
2.2.1 General Corpus
2.2.3 General consideration/definitions used across analysis
3. Descriptives
Justifications and throughts here.
Plot 3: Prevalence of immigration debates over time by month | Total number of words as a proxy for time spent on debating.
Concentration of party-specific contributions
This density plot gives us a sense of the frequency each party discussed each party discussed each month during the time frame of our research. Basically what it does it counts how many words each party each party invested in speaking about immigration related topics. So for example, while the SNP and the DUP spoke more about immigration after Brexit, other parties exhibit a more constant trend of engagement with immigration related speech. Importantly, the information that can be gathered from this graph is limited in that it does not tell us anything about substance of these speeches, but crudely how many words were used. Nevertheless, this descriptive visualization does help us get an initial sense about the prevelance of immigration related speech in each of the parties we are focusing on.
4. Sentiment
Sentiment | Overall Corpus
Graph 1: Overall Sentiment
Graph 2: Sentiment by party
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.5. Sentiment in Context
2.2.2 Create KWIC - Dataframe, Corpus and Dfm
Subset KWIC according to keywords
Sentiment Keywords in Context of keyword
Graph 3: KWIC sentiment #### I think this should also be discarded. It is kind of strange maybe to plot sentiment based on bubbles of 40 words at a time, or, we could justify it by assuming that in these bubbles speeches are likely to be really related to immigration which is also where most sentiment is likely to be voiced. But I am not that convinced by this myself.
